Search Results for author: Tim Oates

Found 45 papers, 11 papers with code

Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection

no code implementations23 Mar 2024 Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt

Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges.

Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks

no code implementations13 Mar 2024 Khondoker Murad Hossain, Tim Oates

In the rapidly evolving landscape of communication and network security, the increasing reliance on deep neural networks (DNNs) and cloud services for data processing presents a significant vulnerability: the potential for backdoors that can be exploited by malicious actors.

Image Classification object-detection +2

TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks

no code implementations6 Jan 2024 Khondoker Murad Hossain, Tim Oates

As deep neural networks and the datasets used to train them get larger, the default approach to integrating them into research and commercial projects is to download a pre-trained model and fine tune it.

Backdoor Attack Tensor Decomposition

Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations

1 code implementation23 Dec 2023 Mohammad Mahmudul Alam, Edward Raff, Tim Oates

While deep learning has enjoyed significant success in computer vision tasks over the past decade, many shortcomings still exist from a Cognitive Science (CogSci) perspective.

LLM Augmented Hierarchical Agents

no code implementations9 Nov 2023 Bharat Prakash, Tim Oates, Tinoosh Mohsenin

However, using LLMs to solve real world problems is hard because they are not grounded in the current task.

In-Context Learning Reinforcement Learning (RL)

cuSLINK: Single-linkage Agglomerative Clustering on the GPU

1 code implementation28 Jun 2023 Corey J. Nolet, Divye Gala, Alex Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates

In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only $O(Nk)$ space and uses a parameter $k$ to trade off space and time.

Clustering graph construction

Recasting Self-Attention with Holographic Reduced Representations

1 code implementation31 May 2023 Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt

In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains.

Malware Detection

Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks

no code implementations15 Dec 2022 Khondoker Murad Hossain, Tim Oates

The increasing importance of both deep neural networks (DNNs) and cloud services for training them means that bad actors have more incentive and opportunity to insert backdoors to alter the behavior of trained models.

Backdoor Attack Image Classification +2

Lempel-Ziv Networks

no code implementations23 Nov 2022 Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff, Tim Oates, James Holt

Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and computationally expensive when applied to very long sequences.

Malware Classification

Towards an Interpretable Hierarchical Agent Framework using Semantic Goals

no code implementations16 Oct 2022 Bharat Prakash, Nicholas Waytowich, Tim Oates, Tinoosh Mohsenin

Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now.

reinforcement-learning Reinforcement Learning (RL)

Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

1 code implementation13 Jun 2022 Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt

Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party's compute environment or hardware is common.

Automatic Goal Generation using Dynamical Distance Learning

no code implementations7 Nov 2021 Bharat Prakash, Nicholas Waytowich, Tinoosh Mohsenin, Tim Oates

In this work, we propose a method for automatic goal generation using a dynamical distance function (DDF) in a self-supervised fashion.

Decision Making Reinforcement Learning (RL)

Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

no code implementations30 Sep 2021 Sourav Mukherjee, David Widmark, Vince DiMascio, Tim Oates

Accurate specification of standard occupational classification (SOC) code is critical to the success of many U. S. work visa applications.

Learning with Holographic Reduced Representations

1 code implementation NeurIPS 2021 Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean

HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space.

Multi-Label Classification Retrieval

Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment

no code implementations Findings (ACL) 2021 Ashwinkumar Ganesan, Francis Ferraro, Tim Oates

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective.

Translation

GPU Semiring Primitives for Sparse Neighborhood Methods

2 code implementations13 Apr 2021 Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, John Zedlewski, Tim Oates

High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations.

BIG-bench Machine Learning Information Retrieval +1

Immigration Document Classification and Automated Response Generation

no code implementations29 Sep 2020 Sourav Mukherjee, Tim Oates, Vince DiMascio, Huguens Jean, Rob Ares, David Widmark, Jaclyn Harder

In this paper, we consider the problem of organizing supporting documents vital to U. S. work visa petitions, as well as responding to Requests For Evidence (RFE) issued by the U. S.~Citizenship and Immigration Services (USCIS).

Classification Document Classification +2

Bringing UMAP Closer to the Speed of Light with GPU Acceleration

1 code implementation1 Aug 2020 Corey J. Nolet, Victor Lafargue, Edward Raff, Thejaswi Nanditale, Tim Oates, John Zedlewski, Joshua Patterson

The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning.

Locality Preserving Loss: Neighbors that Live together, Align together

no code implementations EACL (AdaptNLP) 2021 Ashwinkumar Ganesan, Francis Ferraro, Tim Oates

We present a locality preserving loss (LPL) that improves the alignment between vector space embeddings while separating uncorrelated representations.

Natural Language Inference Sentence Embeddings +3

Using Neural Networks for Programming by Demonstration

no code implementations10 Oct 2019 Karan K. Budhraja, Hang Gao, Tim Oates

A low time-complexity and data requirement favoring framework for reproducing emergent behavior, given an abstract demonstration, is discussed in [1], [2].

Universal Adversarial Perturbation for Text Classification

no code implementations10 Oct 2019 Hang Gao, Tim Oates

Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability.

Adversarial Text General Classification +2

Learning from Observations Using a Single Video Demonstration and Human Feedback

no code implementations29 Sep 2019 Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas Waytowich

In this paper, we present a method for learning from video demonstrations by using human feedback to construct a mapping between the standard representation of the agent and the visual representation of the demonstration.

Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter

no code implementations12 Sep 2019 Chi Zhang, Bryan Wilkinson, Ashwinkumar Ganesan, Tim Oates

Another way to remove that limitation, an optional classification layer, trained on manually annotated DoS attack tweets, to filter out non-attack tweets can be used to increase precision at the expense of recall.

Graph Node Embeddings using Domain-Aware Biased Random Walks

no code implementations8 Aug 2019 Sourav Mukherjee, Tim Oates, Ryan Wright

In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings.

BIG-bench Machine Learning Graph Embedding

Hybrid Mortality Prediction using Multiple Source Systems

no code implementations18 Apr 2019 Isaac Mativo, Yelena Yesha, Michael Grasso, Tim Oates, Qian Zhu

The use of artificial intelligence in clinical care to improve decision support systems is increasing.

Mortality Prediction

Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning

no code implementations28 Mar 2019 Ashwinkumar Ganesan, Pooja Parameshwarappa, Akshay Peshave, ZhiYuan Chen, Tim Oates

In this paper, we proposeaprobabilistic abductive reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to detect these evolved attacks by (a)Predicting rule conditions that are likely to occur (based on existingrules) and (b) able to generate new snort rules when provided withseed rule (i. e. a starting rule) to reduce the burden on experts toconstantly update them.

Intrusion Detection

Automated Cloud Provisioning on AWS using Deep Reinforcement Learning

1 code implementation13 Sep 2017 Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi

As the use of cloud computing continues to rise, controlling cost becomes increasingly important.

Cloud Computing Q-Learning +3

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection

no code implementations28 Aug 2017 JT Turner, Adam Page, Tinoosh Mohsenin, Tim Oates

Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors.

EEG Electroencephalogram (EEG) +2

Fashioning with Networks: Neural Style Transfer to Design Clothes

no code implementations31 Jul 2017 Prutha Date, Ashwinkumar Ganesan, Tim Oates

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis.

Image Segmentation object-detection +5

Identifying Spatial Relations in Images using Convolutional Neural Networks

no code implementations13 Jun 2017 Mandar Haldekar, Ashwinkumar Ganesan, Tim Oates

Traditional approaches to building a large scale knowledge graph have usually relied on extracting information (entities, their properties, and relations between them) from unstructured text (e. g. Dbpedia).

Neuroevolution-Based Inverse Reinforcement Learning

no code implementations9 Aug 2016 Karan K. Budhraja, Tim Oates

One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards.

reinforcement-learning Reinforcement Learning (RL)

Adopting Robustness and Optimality in Fitting and Learning

no code implementations13 Oct 2015 Zhiguang Wang, Tim Oates, James Lo

We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index $\lambda$ to $-\infty$ for achieving robustness to outliers by optimizing a quasi-Minimin function.

Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks

no code implementations24 Sep 2015 Zhiguang Wang, Tim Oates

We propose an off-line approach to explicitly encode temporal patterns spatially as different types of images, namely, Gramian Angular Fields and Markov Transition Fields.

Classification General Classification +2

Adaptive Normalized Risk-Averting Training For Deep Neural Networks

no code implementations8 Jun 2015 Zhiguang Wang, Tim Oates, James Lo

This paper proposes a set of new error criteria and learning approaches, Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex optimization problem in training deep neural networks (DNNs).

Imaging Time-Series to Improve Classification and Imputation

4 code implementations1 Jun 2015 Zhiguang Wang, Tim Oates

We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.

Classification General Classification +4

Detecting Epileptic Seizures from EEG Data using Neural Networks

no code implementations19 Dec 2014 Siddharth Pramod, Adam Page, Tinoosh Mohsenin, Tim Oates

We explore the use of neural networks trained with dropout in predicting epileptic seizures from electroencephalographic data (scalp EEG).

EEG Electroencephalogram (EEG) +1

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